This article demonstrates integrating Google Maps with natural language using the Gemini CLI and an MCP server. This powerful combination allows users to automate complex location-based tasks, such as route planning and information retrieval, through simple, intuitive text-based prompts.
Kanshi TANAIKE tanaikech
A Fake-Sandbox for Google Apps Script: A Feasibility Study on Securely Executing Code Generated by Gemini CLI
Generating Google Apps Script (GAS) with Gemini CLI from natural language introduces security risks due to broad permissions. This report investigates a "Fake-Sandbox" using the gas-fakes library, translating GAS calls into granularly-scoped API requests to securely execute scripts created from user prompts.
This report introduces a powerful method for automating Google Analytics tasks using the Gemini CLI and a custom MCP (Model Context Protocol) server built with Google Apps Script. This integration enables streamlined web page analysis through simple natural language commands, simplifying authorization and complex data retrieval workflows.
This document demonstrates a transformative method for unifying Google Workspace applications by using natural language. Through the integration of the Gemini CLI with MCP, this approach empowers users to intuitively manage Google Drive, Gmail, Google Calendar, Drive Activity, and Google People. Complex tasks and collaborative workflows are streamlined into simple, conversational text commands.
This report provides a comprehensive overview of how to utilize prompts within the Gemini Command-Line Interface (CLI). Leveraging a Google Apps Script MCP server, we will explore practical examples, including roadmap generation, real-time weather inquiries, and Google Drive file searches. This enhanced document offers more in-depth explanations and a broader context to empower users in their understanding and application of these powerful features.
The Model Context Protocol (MCP) establishes a standardized framework for servers to offer clients predefined, structured prompt templates. These user-controllable prompts, customizable with arguments, are engineered to streamline interactions with large language models. The Gemini CLI, starting with version v0.1.15, integrates support for these prompts, significantly expanding its capabilities.
This report explores an optimized approach to integrating the Gemini CLI with Google Workspace via an MCP server. Traditionally, this process requires numerous custom tools, which increases development costs. We propose leveraging the inherent JSON schema requirements of the MCP server tools to directly construct request bodies for the batchUpdate methods of the Google Docs, Sheets, and Slides APIs. This approach aims to consolidate document management into just three core tools, significantly streamlining development and offering a scalable, cost-effective solution for Google Workspace automation and broader API integrations.
This article explores the integration of the Gemini Command-Line Interface (CLI) with Google Sheets using the Model Context Protocol (MCP). It demonstrates how to leverage the open-source projects MCPApp and ToolsForMCPServer to create a bridge between the Gemini CLI and Google Workspace. This enables users to perform powerful data automation tasks, such as creating, reading, and modifying tables in Google Sheets directly from the command line, using natural language prompts. The article provides practical examples and sample prompts to illustrate the seamless workflow and potential for building sophisticated, AI-powered applications within the Google Cloud ecosystem.
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